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All Journal International Journal of Integrative Sciences
Saputra, Riski Yoga
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering Social Sciences

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Application of Expert System in Rice Seedling Selection Based on Smart Data With Methods: Knowledge-Based System and Decision Tree Santi Rahayu; Rosyani, Perani; Saputra, Riski Yoga; Umar, Restu Aji; Prasdio, Sendy; Syach, Wahyu Addiyan
International Journal of Integrative Sciences Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ijis.v4i1.13510

Abstract

The selection of quality rice seeds is vital for maximizing agricultural productivity and sustainability. This study develops an expert system for rice seed selection based on intelligent data processing using the Knowledge-Based System (KBS) and Decision Tree methods. KBS encodes expert knowledge to evaluate seed quality and environmental compatibility, while Decision Tree algorithms classify and predict optimal seed choices. Experimental results demonstrate the system's accuracy in recommending suitable seeds, reducing selection time and effort. This research highlights the potential of artificial intelligence in enhancing decision-making processes in modern agriculture.
Classification of Pneumonia Medical Images with Convolutional Neural Networks Ines Heidiani Ikasari; Saputra, Riski Yoga; Prasdio, Sendy; Kurniagis, Muhammad Faisal; Rosyani, Perani; Janariandana, Zainul
International Journal of Integrative Sciences Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ijis.v4i1.13511

Abstract

Indonesia's agricultural sector faces significant challenges in maintaining rice production due to land conversion, pest attacks, and poor irrigation. Early detection of rice leaf diseases is critical to mitigating these challenges. This study applies the Random Forest (RF) algorithm to classify three rice leaf diseases: Bacterial Leaf Blight, Brown Spot, and Leaf Smut. The proposed method achieved an accuracy of 75%, demonstrating its effectiveness in disease detection. This research provides a foundation for integrating machine learning to improve crop management and agricultural productivity
Co-Authors Ines Heidiani Ikasari Janariandana, Zainul Kurniagis, Muhammad Faisal Perani Rosyani Prasdio, Sendy Santi Rahayu Syach, Wahyu Addiyan Umar, Restu Aji